基于Pytorch实现的声纹识别系统

前言

本项目使用了EcapaTdnn、ResNetSE、ERes2Net、CAM++等多种先进的声纹识别模型,不排除以后会支持更多模型,同时本项目也支持了MelSpectrogram、Spectrogram、MFCC、Fbank等多种数据预处理方法,使用了ArcFace Loss,ArcFace loss:Additive Angular Margin Loss(加性角度间隔损失函数),对应项目中的AAMLoss,对特征向量和权重归一化,对θ加上角度间隔m,角度间隔比余弦间隔在对角度的影响更加直接,除此之外,还支持AMLoss、ARMLoss、CELoss等多种损失函数。

源码地址:VoiceprintRecognition-Pytorch

使用环境:

  • Anaconda 3
  • Python 3.8
  • Pytorch 1.13.1
  • Windows 10 or Ubuntu 18.04

项目特性

  1. 支持模型:EcapaTdnn、TDNN、Res2Net、ResNetSE、ERes2Net、CAM++
  2. 支持池化层:AttentiveStatsPool(ASP)、SelfAttentivePooling(SAP)、TemporalStatisticsPooling(TSP)、TemporalAveragePooling(TAP)、TemporalStatsPool(TSTP)
  3. 支持损失函数:AAMLoss、AMLoss、ARMLoss、CELoss
  4. 支持预处理方法:MelSpectrogram、Spectrogram、MFCC、Fbank

模型论文:

  • EcapaTdnn:ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification
  • PANNS:PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
  • TDNN:Prediction of speech intelligibility with DNN-based performance measures
  • Res2Net:Res2Net: A New Multi-scale Backbone Architecture
  • ResNetSE:Squeeze-and-Excitation Networks
  • CAMPPlus:CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking
  • ERes2Net:An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification

模型下载

模型 Params(M) 预处理方法 数据集 train speakers threshold EER MinDCF
CAM++ 7.5 Fbank CN-Celeb 2796 0.26 0.09557 0.53516
ERes2Net 8.2 Fbank CN-Celeb 2796
ResNetSE 9.4 Fbank CN-Celeb 2796 0.20 0.10149 0.55185
EcapaTdnn 6.7 Fbank CN-Celeb 2796 0.24 0.10163 0.56543
TDNN 3.2 Fbank CN-Celeb 2796 0.23 0.12182 0.62141
Res2Net 6.6 Fbank CN-Celeb 2796 0.22 0.14390 0.67961
ERes2Net 8.2 Fbank 其他数据集 20W 0.36 0.02936 0.18355
CAM++ 7.5 Fbank 其他数据集 20W 0.29 0.04765 0.31436

说明:

  1. 评估的测试集为CN-Celeb的测试集,包含196个说话人。

安装环境

  • 首先安装的是Pytorch的GPU版本,如果已经安装过了,请跳过。
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
  • 安装ppvector库。

使用pip安装,命令如下:

python -m pip install mvector -U -i https://pypi.tuna.tsinghua.edu.cn/simple

建议源码安装,源码安装能保证使用最新代码。

git clone https://github.com/yeyupiaoling/VoiceprintRecognition-Pytorch.git
cd VoiceprintRecognition-Pytorch/
python setup.py install

创建数据

本教程笔者使用的是CN-Celeb,这个数据集一共有约3000个人的语音数据,有65W+条语音数据,下载之后要解压数据集到dataset目录,另外如果要评估,还需要下载CN-Celeb的测试集。如果读者有其他更好的数据集,可以混合在一起使用,但最好是要用python的工具模块aukit处理音频,降噪和去除静音。

首先是创建一个数据列表,数据列表的格式为<语音文件路径\t语音分类标签>,创建这个列表主要是方便之后的读取,也是方便读取使用其他的语音数据集,语音分类标签是指说话人的唯一ID,不同的语音数据集,可以通过编写对应的生成数据列表的函数,把这些数据集都写在同一个数据列表中。

执行create_data.py程序完成数据准备。

python create_data.py

执行上面的程序之后,会生成以下的数据格式,如果要自定义数据,参考如下数据列表,前面是音频的相对路径,后面的是该音频对应的说话人的标签,就跟分类一样。自定义数据集的注意,测试数据列表的ID可以不用跟训练的ID一样,也就是说测试的数据的说话人可以不用出现在训练集,只要保证测试数据列表中同一个人相同的ID即可。

dataset/CN-Celeb2_flac/data/id11999/recitation-03-019.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-10-023.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-06-025.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-04-014.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-06-030.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-10-032.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-06-028.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-10-031.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-05-003.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-04-017.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-10-016.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-09-001.flac      2795
dataset/CN-Celeb2_flac/data/id11999/recitation-05-010.flac      2795

修改预处理方法

配置文件中默认使用的是Fbank预处理方法,如果要使用其他预处理方法,可以修改配置文件中的安装下面方式修改,具体的值可以根据自己情况修改。如果不清楚如何设置参数,可以直接删除该部分,直接使用默认值。

# 数据预处理参数
preprocess_conf:
  # 音频预处理方法,支持:MelSpectrogram、Spectrogram、MFCC、Fbank
  feature_method: 'Fbank'
  # 设置API参数,更参数查看对应API,不清楚的可以直接删除该部分,直接使用默认值
  method_args:
    sample_frequency: 16000
    num_mel_bins: 80

训练模型

使用train.py训练模型,本项目支持多个音频预处理方式,通过configs/ecapa_tdnn.yml配置文件的参数preprocess_conf.feature_method可以指定,MelSpectrogram为梅尔频谱,Spectrogram为语谱图,MFCC梅尔频谱倒谱系数等等。通过参数augment_conf_path可以指定数据增强方式。训练过程中,会使用VisualDL保存训练日志,通过启动VisualDL可以随时查看训练结果,启动命令visualdl --logdir=log --host 0.0.0.0

# 单卡训练
CUDA_VISIBLE_DEVICES=0 python train.py
# 多卡训练
CUDA_VISIBLE_DEVICES=0,1 torchrun --standalone --nnodes=1 --nproc_per_node=2 train.py

训练输出日志:

[2023-08-05 09:52:06.497988 INFO   ] utils:print_arguments:13 - ----------- 额外配置参数 -----------
[2023-08-05 09:52:06.498094 INFO   ] utils:print_arguments:15 - configs: configs/ecapa_tdnn.yml
[2023-08-05 09:52:06.498149 INFO   ] utils:print_arguments:15 - do_eval: True
[2023-08-05 09:52:06.498191 INFO   ] utils:print_arguments:15 - local_rank: 0
[2023-08-05 09:52:06.498230 INFO   ] utils:print_arguments:15 - pretrained_model: None
[2023-08-05 09:52:06.498269 INFO   ] utils:print_arguments:15 - resume_model: None
[2023-08-05 09:52:06.498306 INFO   ] utils:print_arguments:15 - save_model_path: models/
[2023-08-05 09:52:06.498342 INFO   ] utils:print_arguments:15 - use_gpu: True
[2023-08-05 09:52:06.498378 INFO   ] utils:print_arguments:16 - ------------------------------------------------
[2023-08-05 09:52:06.513761 INFO   ] utils:print_arguments:18 - ----------- 配置文件参数 -----------
[2023-08-05 09:52:06.513906 INFO   ] utils:print_arguments:21 - dataset_conf:
[2023-08-05 09:52:06.513957 INFO   ] utils:print_arguments:24 -         dataLoader:
[2023-08-05 09:52:06.513995 INFO   ] utils:print_arguments:26 -                 batch_size: 64
[2023-08-05 09:52:06.514031 INFO   ] utils:print_arguments:26 -                 num_workers: 4
[2023-08-05 09:52:06.514066 INFO   ] utils:print_arguments:28 -         do_vad: False
[2023-08-05 09:52:06.514101 INFO   ] utils:print_arguments:28 -         enroll_list: dataset/enroll_list.txt
[2023-08-05 09:52:06.514135 INFO   ] utils:print_arguments:24 -         eval_conf:
[2023-08-05 09:52:06.514169 INFO   ] utils:print_arguments:26 -                 batch_size: 1
[2023-08-05 09:52:06.514203 INFO   ] utils:print_arguments:26 -                 max_duration: 20
[2023-08-05 09:52:06.514237 INFO   ] utils:print_arguments:28 -         max_duration: 3
[2023-08-05 09:52:06.514274 INFO   ] utils:print_arguments:28 -         min_duration: 0.5
[2023-08-05 09:52:06.514308 INFO   ] utils:print_arguments:28 -         noise_aug_prob: 0.2
[2023-08-05 09:52:06.514342 INFO   ] utils:print_arguments:28 -         noise_dir: dataset/noise
[2023-08-05 09:52:06.514374 INFO   ] utils:print_arguments:28 -         num_speakers: 3242
[2023-08-05 09:52:06.514408 INFO   ] utils:print_arguments:28 -         sample_rate: 16000
[2023-08-05 09:52:06.514441 INFO   ] utils:print_arguments:28 -         speed_perturb: True
[2023-08-05 09:52:06.514475 INFO   ] utils:print_arguments:28 -         target_dB: -20
[2023-08-05 09:52:06.514508 INFO   ] utils:print_arguments:28 -         train_list: dataset/train_list.txt
[2023-08-05 09:52:06.514542 INFO   ] utils:print_arguments:28 -         trials_list: dataset/trials_list.txt
[2023-08-05 09:52:06.514575 INFO   ] utils:print_arguments:28 -         use_dB_normalization: True
[2023-08-05 09:52:06.514609 INFO   ] utils:print_arguments:21 - loss_conf:
[2023-08-05 09:52:06.514643 INFO   ] utils:print_arguments:24 -         args:
[2023-08-05 09:52:06.514678 INFO   ] utils:print_arguments:26 -                 easy_margin: False
[2023-08-05 09:52:06.514713 INFO   ] utils:print_arguments:26 -                 margin: 0.2
[2023-08-05 09:52:06.514746 INFO   ] utils:print_arguments:26 -                 scale: 32
[2023-08-05 09:52:06.514779 INFO   ] utils:print_arguments:24 -         margin_scheduler_args:
[2023-08-05 09:52:06.514814 INFO   ] utils:print_arguments:26 -                 final_margin: 0.3
[2023-08-05 09:52:06.514848 INFO   ] utils:print_arguments:28 -         use_loss: AAMLoss
[2023-08-05 09:52:06.514882 INFO   ] utils:print_arguments:28 -         use_margin_scheduler: True
[2023-08-05 09:52:06.514915 INFO   ] utils:print_arguments:21 - model_conf:
[2023-08-05 09:52:06.514950 INFO   ] utils:print_arguments:24 -         backbone:
[2023-08-05 09:52:06.514984 INFO   ] utils:print_arguments:26 -                 embd_dim: 192
[2023-08-05 09:52:06.515017 INFO   ] utils:print_arguments:26 -                 pooling_type: ASP
[2023-08-05 09:52:06.515050 INFO   ] utils:print_arguments:24 -         classifier:
[2023-08-05 09:52:06.515084 INFO   ] utils:print_arguments:26 -                 num_blocks: 0
[2023-08-05 09:52:06.515118 INFO   ] utils:print_arguments:21 - optimizer_conf:
[2023-08-05 09:52:06.515154 INFO   ] utils:print_arguments:28 -         learning_rate: 0.001
[2023-08-05 09:52:06.515188 INFO   ] utils:print_arguments:28 -         optimizer: Adam
[2023-08-05 09:52:06.515221 INFO   ] utils:print_arguments:28 -         scheduler: CosineAnnealingLR
[2023-08-05 09:52:06.515254 INFO   ] utils:print_arguments:28 -         scheduler_args: None
[2023-08-05 09:52:06.515289 INFO   ] utils:print_arguments:28 -         weight_decay: 1e-06
[2023-08-05 09:52:06.515323 INFO   ] utils:print_arguments:21 - preprocess_conf:
[2023-08-05 09:52:06.515357 INFO   ] utils:print_arguments:28 -         feature_method: MelSpectrogram
[2023-08-05 09:52:06.515390 INFO   ] utils:print_arguments:24 -         method_args:
[2023-08-05 09:52:06.515426 INFO   ] utils:print_arguments:26 -                 f_max: 14000.0
[2023-08-05 09:52:06.515460 INFO   ] utils:print_arguments:26 -                 f_min: 50.0
[2023-08-05 09:52:06.515493 INFO   ] utils:print_arguments:26 -                 hop_length: 320
[2023-08-05 09:52:06.515527 INFO   ] utils:print_arguments:26 -                 n_fft: 1024
[2023-08-05 09:52:06.515560 INFO   ] utils:print_arguments:26 -                 n_mels: 64
[2023-08-05 09:52:06.515593 INFO   ] utils:print_arguments:26 -                 sample_rate: 16000
[2023-08-05 09:52:06.515626 INFO   ] utils:print_arguments:26 -                 win_length: 1024
[2023-08-05 09:52:06.515660 INFO   ] utils:print_arguments:21 - train_conf:
[2023-08-05 09:52:06.515694 INFO   ] utils:print_arguments:28 -         log_interval: 100
[2023-08-05 09:52:06.515728 INFO   ] utils:print_arguments:28 -         max_epoch: 30
[2023-08-05 09:52:06.515761 INFO   ] utils:print_arguments:30 - use_model: EcapaTdnn
[2023-08-05 09:52:06.515794 INFO   ] utils:print_arguments:31 - ------------------------------------------------
······
===============================================================================================
Layer (type:depth-idx)                        Output Shape              Param #
===============================================================================================
Sequential                                    [1, 9726]                 --
├─EcapaTdnn: 1-1                              [1, 192]                  --
│    └─Conv1dReluBn: 2-1                      [1, 512, 98]              --
│    │    └─Conv1d: 3-1                       [1, 512, 98]              163,840
│    │    └─BatchNorm1d: 3-2                  [1, 512, 98]              1,024
│    └─Sequential: 2-2                        [1, 512, 98]              --
│    │    └─Conv1dReluBn: 3-3                 [1, 512, 98]              263,168
│    │    └─Res2Conv1dReluBn: 3-4             [1, 512, 98]              86,912
│    │    └─Conv1dReluBn: 3-5                 [1, 512, 98]              263,168
│    │    └─SE_Connect: 3-6                   [1, 512, 98]              262,912
│    └─Sequential: 2-3                        [1, 512, 98]              --
│    │    └─Conv1dReluBn: 3-7                 [1, 512, 98]              263,168
│    │    └─Res2Conv1dReluBn: 3-8             [1, 512, 98]              86,912
│    │    └─Conv1dReluBn: 3-9                 [1, 512, 98]              263,168
│    │    └─SE_Connect: 3-10                  [1, 512, 98]              262,912
│    └─Sequential: 2-4                        [1, 512, 98]              --
│    │    └─Conv1dReluBn: 3-11                [1, 512, 98]              263,168
│    │    └─Res2Conv1dReluBn: 3-12            [1, 512, 98]              86,912
│    │    └─Conv1dReluBn: 3-13                [1, 512, 98]              263,168
│    │    └─SE_Connect: 3-14                  [1, 512, 98]              262,912
│    └─Conv1d: 2-5                            [1, 1536, 98]             2,360,832
│    └─AttentiveStatsPool: 2-6                [1, 3072]                 --
│    │    └─Conv1d: 3-15                      [1, 128, 98]              196,736
│    │    └─Conv1d: 3-16                      [1, 1536, 98]             198,144
│    └─BatchNorm1d: 2-7                       [1, 3072]                 6,144
│    └─Linear: 2-8                            [1, 192]                  590,016
│    └─BatchNorm1d: 2-9                       [1, 192]                  384
├─SpeakerIdentification: 1-2                  [1, 9726]                 1,867,392
===============================================================================================
Total params: 8,012,992
Trainable params: 8,012,992
Non-trainable params: 0
Total mult-adds (M): 468.81
===============================================================================================
Input size (MB): 0.03
Forward/backward pass size (MB): 10.36
Params size (MB): 32.05
Estimated Total Size (MB): 42.44
===============================================================================================
[2023-08-05 09:52:08.084231 INFO   ] trainer:train:388 - 训练数据:874175
[2023-08-05 09:52:09.186542 INFO   ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [0/13659], loss: 11.95824, accuracy: 0.00000, learning rate: 0.00100000, speed: 58.09 data/sec, eta: 5 days, 5:24:08
[2023-08-05 09:52:22.477905 INFO   ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [100/13659], loss: 10.35675, accuracy: 0.00278, learning rate: 0.00100000, speed: 481.65 data/sec, eta: 15:07:15
[2023-08-05 09:52:35.948581 INFO   ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [200/13659], loss: 10.22089, accuracy: 0.00505, learning rate: 0.00100000, speed: 475.27 data/sec, eta: 15:19:12
[2023-08-05 09:52:49.249098 INFO   ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [300/13659], loss: 10.00268, accuracy: 0.00706, learning rate: 0.00100000, speed: 481.45 data/sec, eta: 15:07:11
[2023-08-05 09:53:03.716015 INFO   ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [400/13659], loss: 9.76052, accuracy: 0.00830, learning rate: 0.00100000, speed: 442.74 data/sec, eta: 16:26:16
[2023-08-05 09:53:18.258807 INFO   ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [500/13659], loss: 9.50189, accuracy: 0.01060, learning rate: 0.00100000, speed: 440.46 data/sec, eta: 16:31:08
[2023-08-05 09:53:31.618354 INFO   ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [600/13659], loss: 9.26083, accuracy: 0.01256, learning rate: 0.00100000, speed: 479.50 data/sec, eta: 15:10:12
[2023-08-05 09:53:45.439642 INFO   ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [700/13659], loss: 9.03548, accuracy: 0.01449, learning rate: 0.00099999, speed: 463.63 data/sec, eta: 15:41:08

VisualDL页面:
基于Pytorch实现的声纹识别系统_第1张图片

评估模型

训练结束之后会保存预测模型,我们用预测模型来预测测试集中的音频特征,然后使用音频特征进行两两对比,计算EER和MinDCF。

python eval.py

输出类似如下:

······
------------------------------------------------
W0425 08:27:32.057426 17654 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 11.6, Runtime API Version: 10.2
W0425 08:27:32.065165 17654 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[2023-03-16 20:20:47.195908 INFO   ] trainer:evaluate:341 - 成功加载模型:models/EcapaTdnn_Fbank/best_model/model.pth
100%|███████████████████████████| 84/84 [00:28<00:00,  2.95it/s]
开始两两对比音频特征...
100%|███████████████████████████| 5332/5332 [00:05<00:00, 1027.83it/s]
评估消耗时间:65s,threshold:0.26,EER: 0.14739, MinDCF: 0.41999

声纹对比

下面开始实现声纹对比,创建infer_contrast.py程序,编写infer()函数,在编写模型的时候,模型是有两个输出的,第一个是模型的分类输出,第二个是音频特征输出。所以在这里要输出的是音频的特征值,有了音频的特征值就可以做声纹识别了。我们输入两个语音,通过预测函数获取他们的特征数据,使用这个特征数据可以求他们的对角余弦值,得到的结果可以作为他们相识度。对于这个相识度的阈值threshold,读者可以根据自己项目的准确度要求进行修改。

python infer_contrast.py --audio_path1=audio/a_1.wav --audio_path2=audio/b_2.wav

输出类似如下:

[2023-04-02 18:30:48.009149 INFO   ] utils:print_arguments:13 - ----------- 额外配置参数 -----------
[2023-04-02 18:30:48.009149 INFO   ] utils:print_arguments:15 - audio_path1: dataset/a_1.wav
[2023-04-02 18:30:48.009149 INFO   ] utils:print_arguments:15 - audio_path2: dataset/b_2.wav
[2023-04-02 18:30:48.009149 INFO   ] utils:print_arguments:15 - configs: configs/ecapa_tdnn.yml
[2023-04-02 18:30:48.009149 INFO   ] utils:print_arguments:15 - model_path: models/EcapaTdnn_Fbank/best_model/
[2023-04-02 18:30:48.009149 INFO   ] utils:print_arguments:15 - threshold: 0.6
[2023-04-02 18:30:48.009149 INFO   ] utils:print_arguments:15 - use_gpu: True
[2023-04-02 18:30:48.009149 INFO   ] utils:print_arguments:16 - ------------------------------------------------
······································································
W0425 08:29:10.006249 21121 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 11.6, Runtime API Version: 10.2
W0425 08:29:10.008555 21121 device_context.cc:465] device: 0, cuDNN Version: 7.6.
成功加载模型参数和优化方法参数:models/EcapaTdnn_Fbank/best_model/model.pth
audio/a_1.wav 和 audio/b_2.wav 不是同一个人,相似度为:-0.09565544128417969

声纹识别

在上面的声纹对比的基础上,我们创建infer_recognition.py实现声纹识别。同样是使用上面声纹对比的infer()预测函数,通过这两个同样获取语音的特征数据。 不同的是笔者增加了load_audio_db()register(),以及recognition(),第一个函数是加载声纹库中的语音数据,这些音频就是相当于已经注册的用户,他们注册的语音数据会存放在这里,如果有用户需要通过声纹登录,就需要拿到用户的语音和语音库中的语音进行声纹对比,如果对比成功,那就相当于登录成功并且获取用户注册时的信息数据。第二个函数register()其实就是把录音保存在声纹库中,同时获取该音频的特征添加到待对比的数据特征中。最后recognition()函数中,这个函数就是将输入的语音和语音库中的语音一一对比。
有了上面的声纹识别的函数,读者可以根据自己项目的需求完成声纹识别的方式,例如笔者下面提供的是通过录音来完成声纹识别。首先必须要加载语音库中的语音,语音库文件夹为audio_db,然后用户回车后录音3秒钟,然后程序会自动录音,并使用录音到的音频进行声纹识别,去匹配语音库中的语音,获取用户的信息。通过这样方式,读者也可以修改成通过服务请求的方式完成声纹识别,例如提供一个API供APP调用,用户在APP上通过声纹登录时,把录音到的语音发送到后端完成声纹识别,再把结果返回给APP,前提是用户已经使用语音注册,并成功把语音数据存放在audio_db文件夹中。

python infer_recognition.py

输出类似如下:

[2023-04-02 18:31:20.521040 INFO   ] utils:print_arguments:13 - ----------- 额外配置参数 -----------
[2023-04-02 18:31:20.521040 INFO   ] utils:print_arguments:15 - audio_db_path: audio_db/
[2023-04-02 18:31:20.521040 INFO   ] utils:print_arguments:15 - configs: configs/ecapa_tdnn.yml
[2023-04-02 18:31:20.521040 INFO   ] utils:print_arguments:15 - model_path: models/EcapaTdnn_Fbank/best_model/
[2023-04-02 18:31:20.521040 INFO   ] utils:print_arguments:15 - record_seconds: 3
[2023-04-02 18:31:20.521040 INFO   ] utils:print_arguments:15 - threshold: 0.6
[2023-04-02 18:31:20.521040 INFO   ] utils:print_arguments:15 - use_gpu: True
[2023-04-02 18:31:20.521040 INFO   ] utils:print_arguments:16 - ------------------------------------------------
······································································
W0425 08:30:13.257884 23889 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 11.6, Runtime API Version: 10.2
W0425 08:30:13.260191 23889 device_context.cc:465] device: 0, cuDNN Version: 7.6.
成功加载模型参数和优化方法参数:models/ecapa_tdnn/model.pth
Loaded 沙瑞金 audio.
Loaded 李达康 audio.
请选择功能,0为注册音频到声纹库,1为执行声纹识别:0
按下回车键开机录音,录音3秒中:
开始录音......
录音已结束!
请输入该音频用户的名称:夜雨飘零
请选择功能,0为注册音频到声纹库,1为执行声纹识别:1
按下回车键开机录音,录音3秒中:
开始录音......
录音已结束!
识别说话的为:夜雨飘零,相似度为:0.920434

其他版本

  • Tensorflow:VoiceprintRecognition-Tensorflow
  • PaddlePaddle:VoiceprintRecognition-PaddlePaddle
  • Keras:VoiceprintRecognition-Keras

参考资料

  1. https://github.com/PaddlePaddle/PaddleSpeech
  2. https://github.com/yeyupiaoling/PaddlePaddle-MobileFaceNets
  3. https://github.com/yeyupiaoling/PPASR
  4. https://github.com/alibaba-damo-academy/3D-Speaker

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